Applied integration of time series and multi-variable regression algorithms
Fatih Koyuncu, Ahmet YÜCEL · DergiPark (Istanbul University) · 2021
Time Series (TS) based prediction models generate prediction based data that is supposed to be similar to the future data at a certain level.In this study, we designed new modeling that increases the prediction performance of the TS algorithm.The main purpose of the new modeling is to integrate the Multivariate-Adaptive-Regression-Splines (MARSplines) algorithm into the TS algorithm.Five-year Tokyo Stock Exchange data is analyzed as a case study to apply the relevant models.The results show that the new regression-based approach significantly improves the prediction performance of the time series algorithm.